RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process

Understand

An RNN-based forecasting approach is used to early detect anomalies in industrial multivariate time series data from a simulated Tennessee Eastman Process (TEP) with many cyber-attacks.

• This work continues a previously proposed LSTM-based approach to the fault detection in simpler data.
• It is considered necessary to adapt the RNN network to deal with data containing stochastic, stationary, transitive and a rich variety of anomalous behaviours.
• There is particular focus on early detection with special NAB-metric.